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Ai Data Annotation Remote Jobs in Michigan (NOW HIRING)

General Motors

Senior AI/ML Engineer

Lansing, MI · On-site +1

$106K - $145K/yr

Remote/Hybrid: This role is based remotely but if you live within a 50-mile radius of Sunnyvale, CA ... data-annotation pipelines and machine-led training data solutions at foundation-model scale . We ...

Data Annotation

$50 - $60/hr

Join our team to help train the next generation of AI while enjoying the flexibility of remote work ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

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Ai Data Annotation Remote information

What is an AI Data Annotation Remote job?

An AI Data Annotation Remote job involves labeling, tagging, or categorizing data used to train artificial intelligence models. Annotators work with text, images, audio, or video to ensure machine learning algorithms receive accurate and high-quality input. This role is performed remotely, allowing flexibility in work location and schedule. Attention to detail, consistency, and familiarity with annotation tools are essential skills for this job.

What does a typical day look like for someone working remotely in AI data annotation?

A typical day for a remote AI Data Annotation worker involves reviewing and labeling various types of data such as images, text, or audio according to specific guidelines provided by the employer or project lead. You may use specialized annotation software and work through batches of data while following quality standards and deadlines. Periodic team check-ins or virtual meetings help clarify instructions, address questions, and monitor progress. While most of the work is independent, communication with supervisors or quality assurance teams is important to ensure that data labeling is consistent and accurate.

What are the key skills and qualifications needed to thrive in the Ai Data Annotation Remote position, and why are they important?

To thrive as an AI Data Annotation Remote worker, you need strong attention to detail, familiarity with data labeling processes, and a basic understanding of machine learning concepts, often supported by a high school diploma or relevant experience. Familiarity with data annotation platforms such as Labelbox, Supervisely, or AWS SageMaker Ground Truth is typically required, and certifications in data annotation or AI may be advantageous. Strong time management, the ability to work independently, and clear communication skills are valuable in this remote role. These abilities ensure accurate and efficient data labeling, which is critical for training reliable AI models.

What are the most commonly searched types of Ai Data Annotation jobs in Michigan? The most popular types of Ai Data Annotation jobs in Michigan are:
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Ai Data Annotation Remote jobs near you
Infographic showing various Ai Data Annotation Remote job openings in Michigan as of June 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 100% Remote job distribution.
AI Data Scientist

AI Data Scientist

Strategic Staffing Solutions

Detroit, MI • On-site, Remote

Other

Posted 9 days ago

Job description

Job Description STRATEGIC STAFFING SOLUTIONS (S3) HAS AN OPENING. AI Data Scientist Remote in EST/CST W2 contract role 6 Months then eligible for Contract renewal Role Overview The Advanced Analytics Hub team is looking to bring on board an Expert AI Data Scientist for an AI project as a Contractor. The objective of this project is to build an intelligent, agentic AI solution that provides material recommendations from SRM Material Catalogs at the point of purchase requisition, leveraging RAG and LLM-based capabilities.

Key Requirements: End-to-End Agentic RAG System Design - Proven experience designing and deploying production-grade RAG systems, including embeddings, vector search, and agent orchestration for multi-step reasoning workflows. LLM & GenAI Integration at Scale (with Agent Frameworks) - Hands-on expertise integrating LLMs into enterprise applications, including prompt engineering, tool usage, and experience with frameworks such as LangGraph/LangChain, Semantic Kernel, or AutoGen. Retrieval Quality, Evaluation & Optimization - Strong background in evaluation frameworks (precision/recall, grounding accuracy, hallucination detection) and optimization techniques (chunking, re-ranking, hybrid search).

MLOps & Productionalization - Experience deploying AI solutions at scale with CI/CD pipelines, model lifecycle management, monitoring, and cloud environments (Azure preferred). Strong ML & Statistical Foundation - Deep expertise in Python, ML/statistics, and experimentation with a focus on rigorous validation of model performance and business impact. Systems Thinking & Enterprise Integration - Ability to architect and integrate AI solutions within enterprise ecosystems (preferably SAP/SRM or similar procurement workflows).

Vector Databases & Retrieval Infrastructure - Hands-on experience with vector databases (e.g., Azure AI Search, Pinecone, FAISS) and optimization for real-time use cases. Core Engineering Best Practices - Strong proficiency in Python, Git, API development, and modern software engineering practices including CI/CD for ML systems. Experience running local models - we run local models on high compute servers on prem (500 GB RAM, 4 L40s GPUs, 64 cpu running on RHEL 9.7) before deploying the solution on cloud platform to save us the cloud cost during development *Beware of scams

S3 never asks for money during its onboarding process

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